The next-generation sequencing technologies have now brought the dream of individual genome identification close to reality. However, new advances in genome sequencing are necessary but not sufficient for identifying functionally important variants and understanding the origins of many diseases. Specific human phenotype is largely determined by stability, activity, and interactions between numerous biomolecules which work together to provide specific cellular functions. Although the majority of genetic variations are likely to be neutral, a substantial fraction of them might explain the origins of Mendelian and complex diseases. Somatic mutations may contribute significantly to tumorigenesis, and driver mutations may allow cancer cells to sustain proliferative signaling. However, finding functionally important mutations and predicting their molecular mechanisms largely remains an unsolved problem. If a disease is caused by a malfunction of a particular protein, the effects caused by missense mutations can be pinpointed by in silico modeling and it makes it more feasible to find a treatment that will reverse the effect. Signaling networks involve a dense network of protein interactions and at the same time are often deregulated in many diseases including cancer. Therefore the analysis of protein complexes, disease-related interaction networks and the effect of disease mutations on network properties would give us important clues for understanding the molecular mechanisms of diseases and allow their treatment and prevention. In fact many disease mutations are located on protein binding interfaces and may affect the specificity of recognition and protein binding affinity. A missense mutation that alters protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on proteinprotein binding is critical for a wide range of biomedical applications. We developed an efficient computational approach for predicting the effect of single and multiple missense mutations on proteinprotein binding affinity. It is based on a well-tested simulation protocol for structure minimization, modified MM-PBSA and statistical scoring energy functions. Our method yielded good agreement between predicted and experimental values for several thousands of mutations with Pearson correlation coefficient of 0.69. Compared with other available methods, our approach achieved high speed and prediction accuracy and can be applied to large datasets generated by modern genomics initiatives. The monomeric Casitas B-lineage lymphoma RING E3 ligases are emerging therapeutic targets in cancer treatment. RING domain of Cbl has E3 ligase activity and ubiquitinylates activated receptor tyrosine kinases which subsequently targets them for recycling or degradation. At the same time Cbl can bind to activated receptor tyrosine kinases via its TKBD domain and serve as an adaptor by recruiting downstream signal transduction components such as SHP2 and P13K. Therefore proteins from CBL family play both positive and negative regulatory roles in tyrosine kinase signaling which is aberrantly activated in many cancers. CBLs can also bind to the ubiquitin-conjugating enzyme (E2) in a complex with ubiquitin and substrate protein and facilitate the transfer of Ub from E2 to a lysine residue of the substrate. Cbl represents a convenient system to investigate the mechanistic aspects of cancer mutations since several Cbl structures are available representing the snapshots of different stages of Cbl activation cycle. We applied our computational model to predict the effect of Cbl mutations on different Cbl conformational states. Our predictions were tested by performing blind experiments on Cbl-mediated EGFR ubiquitination assays and showed a remarkable agreement between experimental densitometry data and changes in Cbl stability. It could not only quantitatively predict the magnitude of the effects of mutations but at the same time shed light on the mechanisms of their action. We showed both experimentally and computationally that about one third of tested cancer mutations were passenger and did not impact the ubiquitination activity while others, potentially driver mutations, affected different stages of Cbl activation cycle either completely abolishing its ligase activity or partially attenuating it. Nucleosomes represent elementary building blocks of chromatin and unique systems to study protein-protein, protein-DNA binding and principles of their regulation. There are four types of core histones (H3, H4, H2A, H2B) two copies of each forming the nucleosome core particle. Long N-terminal histone tails protrude from the octamer and have many post-translational modification sites, which constitute the so-called histone code. All basic histone types are known to be coded by a set of genes which give rise to a family of histone variants that can all be incorporated into nucleosomes and may have functional and structural significance. It was shown that histone variants can be implicated in many important biological processes including transcription regulation, DNA repair, heterochromatin formation, chromosome segregation and mitosis. Although the determination of nucleosome core particle structure by X-ray crystallography has been a giant leap forward in our understanding of chromatin structure, it is now becoming more evident that the obtained crystal structure does not represent the only possible functionally important conformation. Molecular dynamics simulations of nucleosomes allow to go beyond crystallography in our understanding of nucleosomes by providing a framework to analyze the interactions through a dynamic conformational ensemble, by understanding the behavior of histone tails and by studying the variantions in histone sequences and their dynamical behavior. Our goal is to understand the influence of histone sequence variation on the structure and dynamics of nucleosomes and identify key determinants, which affect nucleosome functioning (i.e. nucleosome stability, inter and intra nucleosomal interactions, DNA dynamics and conformation at local and nucleosome wide scale, conformation of histone tails, nucleosome binding interfaces).